{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 指标系统\n",
    "----\n",
    " - M1、M2、CPI、PPI、PMI、GDP、BDI\n",
    " - 市场人气指标、巴菲特指数\n",
    " - M2与GDP、M2与上交所A股流通市值\n",
    " - 股票盈利收益率、A股市盈率、市净率、股息率\n",
    " - 融资融券、新增信贷、证券市场交易结算资金\n",
    " - A股股票新增开户数、股指期货主力合约\n",
    " - 利率、存款保证金率、Shibor隔夜拆借利率\n",
    " - 10年期国债收益率曲线\n",
    " - 美元指数、美元人民币&离岸"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#导入库\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import sys\n",
    "sys.path.append('../PF')\n",
    "\n",
    "from pf_mcr import Mcr\n",
    "\n",
    "# 聚宽数据\n",
    "import jqdatasdk\n",
    "jqdatasdk.auth(\"13695683829\", \"ssk741212\")\n",
    "from jqdatasdk import *\n",
    "\n",
    "mcr=Mcr('csv','../Data/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 获取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据更新：宏观，无需更新 借利率 \n"
     ]
    }
   ],
   "source": [
    "mcr.data.update(mcr.pool.track)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 生成标的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "_codes={\n",
    "    'CPI':'居民消费价格指数',\n",
    "    'PPI':'工业品出厂价格指数',\n",
    "    'PMI':'采购经理人指数',\n",
    "    'MS':'货币供应量',\n",
    "    'NFC':'新增信贷数据',\n",
    "    'GDP':'国内生产总值',\n",
    "    'SCN':'股票账户新开',\n",
    "    \n",
    "    'SHIBOR':u'上海银行同行业拆借利率',\n",
    "    \n",
    "    'UDI':u'美元指数',\n",
    "    'VIX':u'VIX波动率',\n",
    "    'BDI':u'波罗的海干散货指数',\n",
    "    'C10Y':u'中国十年期国债',\n",
    "    'C5Y':u'中国五年期国债',\n",
    "    'U10Y':u'美国十年期国债',\n",
    "    'U5Y':u'美国五年期国债',\n",
    "    'UCH':'离岸人民币',\n",
    "    'UCY':'在岸人民币',\n",
    "    }\n",
    "\n",
    "\n",
    "# mcr.pool.create(_codes)\n",
    "# mcr.pool.track"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'East' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-5e7403c9c573>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     34\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     35\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 36\u001b[1;33m \u001b[0mget_gdp_chart\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'SCN'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-4-5e7403c9c573>\u001b[0m in \u001b[0;36mget_gdp_chart\u001b[1;34m(code)\u001b[0m\n\u001b[0;32m     21\u001b[0m     \u001b[0mdf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mget_contrast_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;31m#.dropna()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m     \u001b[0mmcr_df\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mEast\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'cost'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     24\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m     \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmcr_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'D'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfill_method\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'ffill'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'East' is not defined"
     ]
    }
   ],
   "source": [
    "def get_contrast_data(code='000902.XSHG'):\n",
    "    #获取收盘行情\n",
    "    df=get_price(code,start_date='2005-01-01',end_date=pd.datetime.today(),fields=['close'])   \n",
    " \n",
    "    #返回数据\n",
    "    return df\n",
    "\n",
    "def data_to_period(df,period='W'):\n",
    "        df['date']=df.index\n",
    "        df=df.resample(period,how='last')\n",
    "        df.index=df['date']\n",
    "        del df['date']\n",
    "        df=df.dropna()\n",
    "        df.index.name=None\n",
    "        return df    \n",
    "\n",
    "    \n",
    "# GDP同比增长与指数对比图        \n",
    "def get_gdp_chart(code):\n",
    "\n",
    "    df=get_contrast_data()#.dropna()\n",
    "\n",
    "    mcr_df=East.get_data(code)[['cost']]\n",
    "    \n",
    "    df[code]=mcr_df.resample('D',fill_method='ffill')\n",
    "\n",
    "    #组织指数数据\n",
    "    df=data_to_period(df,period='W')\n",
    "\n",
    "\n",
    "    \n",
    "    df.plot(figsize=(18,7),secondary_y=['close'],title='',\n",
    "                 linewidth=0.6,mark_right=False,grid=True,style=['b','r'])  \n",
    "    \n",
    "    \n",
    "get_gdp_chart('SCN')    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.宏观经济"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.国内生产总值-GDP\n",
    "---\n",
    "\n",
    "GDP是按市场价格计算的国内生产总值的简称，是指一个国家（或地区）所有常住单位在一定时期内生产活动的最终成果。它涉及的是经济活动，是实实在在的。\n",
    "判断宏观经济运行状况主要有3个重要经济指标，经济增长率、通货膨胀率，失业率，这三个指标都与GDP有密切关系，其中经济增长率就是GDP增长率，通货膨胀率就是GDP紧缩指数，失业率中的奥肯定律表明当GDP增长大于2.25个百分点时，每增加一个百分单位的国内生产总值，失业率就降低0.5个百分点。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'get_gdp_quarter' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-bf7862354f81>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     40\u001b[0m         linewidth=0.6,mark_right=False,style=['b','c','r'])  \n\u001b[0;32m     41\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 42\u001b[1;33m \u001b[0mget_gdp_chart\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     43\u001b[0m \u001b[0mget_gdp_chart2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-6-bf7862354f81>\u001b[0m in \u001b[0;36mget_gdp_chart\u001b[1;34m(code, title)\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mchart_df\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;31m#读取指标数据\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[0mindicator_df\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mget_gdp_quarter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m     \u001b[1;31m#最后更新日期\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[0mlastdate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mindicator_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrftime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'%Y-%m'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'get_gdp_quarter' is not defined"
     ]
    }
   ],
   "source": [
    "#GDP同比增长与指数对比图        \n",
    "def get_gdp_chart(code='GDP',title=''):\n",
    "    #图表DataFrame\n",
    "    chart_df=pd.DataFrame()\n",
    "    #读取指标数据\n",
    "    indicator_df=get_gdp_quarter(code)\n",
    "    #最后更新日期\n",
    "    lastdate=indicator_df.index[-1].strftime('%Y-%m')\n",
    "    #指标最新数据\n",
    "    cpi=indicator_df['gdp_yoy'].iloc[-1] \n",
    "    #组织图表指标数据\n",
    "    chart_df['%s  %.2f'%(code,float(cpi))]=indicator_df['gdp_yoy']\n",
    "    #组织指数数据\n",
    "    contrast_df,contrast_title=get_contrast_data(period='Q')\n",
    "    chart_df[contrast_title]=contrast_df   \n",
    "    #去除多余数据\n",
    "    chart_df=chart_df.dropna()\n",
    "    #图表标题,\n",
    "    chart_title='%s %s - %s （%s）'%(title,code,indicator_dict[code],lastdate)\n",
    "    chart_df.plot(figsize=(18,7),secondary_y=[contrast_title],title=chart_title,\n",
    "                 linewidth=0.6,mark_right=False,grid=True,style=['b','r'])  \n",
    "    \n",
    "#GDP\n",
    "def get_gdp_chart2(code='GDP',title=''):\n",
    "    #图表DataFrame\n",
    "    chart_df=pd.DataFrame()\n",
    "    #读取指标数据\n",
    "    indicator_df=get_gdp_year(code)[['gdp']]\n",
    "    indicator_df=indicator_df[indicator_df.index>='1978']\n",
    "    #最后更新日期\n",
    "    lastdate=indicator_df.index[-1].strftime('%Y年')\n",
    "    #组织图表指标数据\n",
    "    chart_df['GDP  %.2f'%(indicator_df['gdp'].iloc[-1])]=indicator_df['gdp']\n",
    "    #整理数据 \n",
    "    chart_df=chart_df.dropna()\n",
    "    chart_df=chart_df.astype('float32') \n",
    "    #图表标题,\n",
    "    chart_title='%s - %s  %s （%s）'%(code,indicator_dict[code],title,lastdate)\n",
    "    chart_df.plot(kind='bar',figsize=(18,7),title=chart_title,stacked=True,grid=True,\n",
    "        linewidth=0.6,mark_right=False,style=['b','c','r'])  \n",
    "    \n",
    "get_gdp_chart()\n",
    "get_gdp_chart2()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2.货币供应量-M1与M2\n",
    "\n",
    "M0=流通中现金； 狭义货币（M1）=M0＋可开支票进行支付的单位活期存款；  广义货币（M2）=M1＋居民储蓄存款＋单位定期存款＋单位其他存款＋证券公司客户保证金；\n",
    "其中，M2减M1是准货币（Quasi-money）。\n",
    "\n",
    " - M1增加，投资者信心增强，经济活跃度提高，股市和房地产市场上涨；反之，M1减少，股市和房地产市场下跌，因此，对股市和房地产市场具有经济晴雨表功能，并对货币变化具有放大效应。\n",
    " - 如果M1增速大于M2，意味着企业的活期存款增速大于定期存款增速，企业和居民交易活跃，微观主体盈利能力较强，经济景气度上升。\n",
    " - 如果M1增速小于M2，表明企业和居民选择将资金以定期的形式存在银行，微观个体盈利能力下降，未来可选择的投资机会有限，多余的资金开始从实体经济中沉淀下来，经济运行回落。\n",
    " - M1与上证综指的相互影响关系中上证综指的变动作为原因引起M1的变动是占主导地位的，而M1的变动引起上证综指的变动则是次要的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'get_m1m2_month' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-a6fa02bedcb1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     40\u001b[0m         linewidth=0.6,mark_right=False,style=['b','c','r'])  \n\u001b[0;32m     41\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 42\u001b[1;33m \u001b[0mget_m1m2_chart\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'同比增长'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     43\u001b[0m \u001b[0mget_m1m2_chart2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'数量'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-7-a6fa02bedcb1>\u001b[0m in \u001b[0;36mget_m1m2_chart\u001b[1;34m(code, title)\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mchart_df\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;31m#读取指标数据\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[0mindicator_df\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mget_m1m2_month\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'm1_yoy'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'm2_yoy'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m     \u001b[1;31m#最后更新日期\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[0mlastdate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mindicator_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrftime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'%Y-%m'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'get_m1m2_month' is not defined"
     ]
    }
   ],
   "source": [
    "#M0M1同比增长与指数对比图        \n",
    "def get_m1m2_chart(code='M1M2',title='同比增长'):\n",
    "    #图表DataFrame\n",
    "    chart_df=pd.DataFrame()\n",
    "    #读取指标数据\n",
    "    indicator_df=get_m1m2_month(code)[['m1_yoy','m2_yoy']]  \n",
    "    #最后更新日期\n",
    "    lastdate=indicator_df.index[-1].strftime('%Y-%m')\n",
    "    #组织图表指标数据\n",
    "    chart_df['M1同比增长  %.2f%%'%(indicator_df['m1_yoy'].iloc[-1])]=indicator_df['m1_yoy']\n",
    "    chart_df['M2同比增长  %.2f%%'%(indicator_df['m2_yoy'].iloc[-1] )]=indicator_df['m2_yoy']\n",
    "    \n",
    "    #组织指数数据\n",
    "    contrast_df,contrast_title=get_contrast_data()\n",
    "    chart_df[contrast_title]=contrast_df   \n",
    "    #整理数据 \n",
    "    chart_df=chart_df.dropna() \n",
    "    #图表标题,\n",
    "    chart_title='%s %s - %s （%s）'%(code,title,indicator_dict[code],lastdate)\n",
    "    chart_df.plot(figsize=(18,7),secondary_y=[contrast_title],title=chart_title,\n",
    "        linewidth=0.6,mark_right=False,grid=True,style=['b','m','r'])  \n",
    "\n",
    "#M0M1  \n",
    "def get_m1m2_chart2(code='M1M2',title=''):\n",
    "    #图表DataFrame\n",
    "    chart_df=pd.DataFrame()\n",
    "    #读取指标数据\n",
    "    indicator_df=get_m1m2_year(code)\n",
    "    #最后更新日期\n",
    "    lastdate=indicator_df.index[-1].strftime('%Y年')\n",
    "    #组织图表指标数据\n",
    "    chart_df['M1  %.2f'%(indicator_df['m1'].iloc[-1])]=indicator_df['m1']\n",
    "    chart_df['M2  %.2f'%(indicator_df['m2'].iloc[-1])]=indicator_df['m2'] \n",
    "    #整理数据 \n",
    "    chart_df=chart_df.dropna()\n",
    "    chart_df=chart_df.astype('float32') \n",
    "    #图表标题,\n",
    "    chart_title='%s %s - %s （%s）'%(code,title,indicator_dict[code],lastdate)\n",
    "    chart_df.plot(kind='bar',figsize=(18,7),title=chart_title,stacked=True,grid=True,\n",
    "        linewidth=0.6,mark_right=False,style=['b','c','r'])  \n",
    "    \n",
    "get_m1m2_chart(title='同比增长')\n",
    "get_m1m2_chart2(title='数量')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 1.3.消费者物价指数-CPI\n",
    "\n",
    "CPI是消费者物价指数(Consumer Price Index)的简称，是选取一篮子消费品和劳务，以他们在消费者支出中的比重为权数来衡量的市场价格变动率，主要反映消费者支付商品和劳务的价格变化情况，与生产者物价指数PPI一样，是观察通货膨胀水平的重要指标。\n",
    "\n",
    " - 反映通货膨胀水平：通货膨胀的严重程度是用通货膨胀率来反映的，它说明了一定时期内商品价格持续上升的幅度。通货膨胀率一般以消费者物价指数来表示。\n",
    " - 反映货币购买力变动：货币购买力是指单位货币能够购买到的消费品和服务的数量。消费者物价指数上涨，货币购买力则下降；反之则上升。消费者物价指数的倒数就是货币购买力指数。\n",
    " - 反映对职工实际工资的影响：消费者物价指数的提高意味着实际工资的减少，消费者物价指数的下降意味着实际工资的提高。因此实际工资等于名义工资除以消费者物价指数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nH/T6gw8+yI4dO3L3RepdMXny5NzVPmbNmkXXrl2jbtshhxxCxYoVc88/efJk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2C1iY3CCX1SSnvqDBQvTgHWpCQ+NlMI0x+NGgGff05NnjFjjBPLXLkSaNhQuyyvGfjrkw6ZQgfE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\nToXZs+HJJ113PRGRahx8MIwbBytWuJEUx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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa5048a80b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#生成CPI与指数对比图        \n",
    "def get_cpi_chart(code='CPI',title=''):\n",
    "    #图表DataFrame\n",
    "    chart_df=pd.DataFrame()\n",
    "    #读取指标数据\n",
    "    indicator_df=get_cpi_data(code)\n",
    "    #最后更新日期\n",
    "    lastdate=indicator_df.index[-1].strftime('%Y-%m')\n",
    "    #指标最新数据\n",
    "    cpi=indicator_df['cpi'].iloc[-1] \n",
    "    #组织图表指标数据\n",
    "    chart_df['%s  %.2f'%(code,float(cpi))]=indicator_df['cpi']\n",
    "    #组织指数数据\n",
    "    contrast_df,contrast_title=get_contrast_data()\n",
    "    chart_df[contrast_title]=contrast_df   \n",
    "    #去除多余数据\n",
    "    chart_df=chart_df.dropna()\n",
    "    #图表标题,\n",
    "    chart_title='%s %s - %s （%s）'%(title,code,indicator_dict[code],lastdate)\n",
    "    chart_df.plot(figsize=(18,7),secondary_y=[contrast_title],title=chart_title,\n",
    "                 linewidth=0.6,mark_right=False,grid=True,style=['b','r'])  \n",
    "    \n",
    "get_cpi_chart()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4.工业品出厂价格指数-PPI\n",
    "\n",
    "PPI是工业品出厂价格指数（Producer Price Index）的简称，该指数是根据制成品和原料的批发价格指数编制，反映包括原材料、中间品及最终产品在内的各种商品的批发价格的变动状况的物价指数，所以又称为生产者物价指数。与消费者物价指数CPI一样，是观察通货膨胀水平的重要指标。\n",
    "\n",
    " - 优点：是能灵敏地反映生产者生产成本的变化状况;\n",
    " - 缺点：是没有将各种劳务包括之内，且对一般居民的生活影响不够直接。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JFMWkBI1iUoJGMSlBo5iUIFJcBpcxBhtRcyLl91dyQkRERERERCS7+Z2cSEZBzIQYY/Sl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\nWLDA7iiiFxmTgtNY/vPPXDFq3druUCKfKGkTVipzi3nJksDx4963NVt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GpBAM8+YBbdua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\nERHx6v8BpZP8Fb20bVAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa4f116ed30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#生成PPI与指数对比图        \n",
    "def get_ppi_chart(code='PPI',title=''):\n",
    "    #图表DataFrame\n",
    "    chart_df=pd.DataFrame()\n",
    "    #读取指标数据\n",
    "    indicator_df=get_ppi_data(code)\n",
    "    #最后更新日期\n",
    "    lastdate=indicator_df.index[-1].strftime('%Y-%m')\n",
    "    #指标最新数据\n",
    "    cpi=indicator_df['ppi'].iloc[-1] \n",
    "    #组织图表指标数据\n",
    "    chart_df['%s  %.2f'%(code,float(cpi))]=indicator_df['ppi']\n",
    "    #组织指数数据\n",
    "    contrast_df,contrast_title=get_contrast_data()\n",
    "    chart_df[contrast_title]=contrast_df   \n",
    "    #去除多余数据\n",
    "    chart_df=chart_df.dropna()\n",
    "    #图表标题,\n",
    "    chart_title='%s %s - %s （%s）'%(title,code,indicator_dict[code],lastdate)\n",
    "    chart_df.plot(figsize=(18,7),secondary_y=[contrast_title],title=chart_title,\n",
    "                 linewidth=0.6,mark_right=False,grid=True,style=['b','r']) \n",
    "    \n",
    "get_ppi_chart()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5.采购经理指数-PMI\n",
    "PMI是采购经理指数(Purchasing Managers’Index)的简称。中国制造业采购经理指数体系共包括11个指数：新订单、生产、就业、供应商配送、存货、新出口订单、采购、产成品库存、购进价格、进口、积压订单。\n",
    "\n",
    " - PMI指数是经济监测的先行指标。\n",
    " - 综合指数反映了经济总体情况和总的变化趋势，而各项指标又反映了企业供应与采购活动的各个侧面。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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//Q+A9PR0Bg0aVGZsL7zwAuPHjy9dpTB+/PiA45g3bx5r164tXYGgtS4NYKxa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\nghX1QMT69dC+fbTPGlj51p2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syAhXMgQiEoHW8Mor8Pe/w8svw4ABsRtLoI444aTlxrJO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\nPIhM605PNWvC0aP2HCtQrYBI27Urm0aNzKoHb06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8UZQoQT/L06ep3oNSe/fSOdiqeEUE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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa4e55bc6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#生成PPI与指数对比图        \n",
    "def get_pmi_chart(code='PMI',title=''):\n",
    "    #图表DataFrame\n",
    "    chart_df=pd.DataFrame()\n",
    "    #读取指标数据\n",
    "    indicator_df=get_pmi_data(code)\n",
    "    #最后更新日期\n",
    "    lastdate=indicator_df.index[-1].strftime('%Y-%m')\n",
    "    #组织图表指标数据\n",
    "    chart_df['%s  %.2f'%(code,indicator_df['pmi'].iloc[-1])]=indicator_df['pmi']\n",
    "    #组织指数数据\n",
    "    contrast_df,contrast_title=get_contrast_data()\n",
    "    chart_df[contrast_title]=contrast_df   \n",
    "    #去除多余数据\n",
    "    chart_df=chart_df.dropna()\n",
    "    #图表标题,\n",
    "    chart_title='%s %s - %s （%s）'%(title,code,indicator_dict[code],lastdate)\n",
    "    chart_df.plot(figsize=(18,7),secondary_y=[contrast_title],title=chart_title,\n",
    "                 linewidth=0.6,mark_right=False,grid=True,style=['b','r']) \n",
    "    \n",
    "get_pmi_chart()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.6.经济金融化-M2与GDP之比\n",
    "--------\n",
    "\n",
    " - 通常认为，这一指标比例反映了一个经济的金融深度。但M2/GDP比例的大小、趋势和原因则受到多种不同因素的影响。M2/GDP实际衡量的是在全部经济交易中，以货币为媒介进行交易所占的比重。\n",
    " - 总体上看，它是衡量一国经济金融化的初级指标。通常来说，该比值越大，说明经济货币化的程度越高。"
   ]
  },
  {
   "source": [
    "#GDP同比增长与指数对比图        \n",
    "def get_jrh_chart(code='',title=''):\n",
    "    #图表DataFrame\n",
    "    chart_df=pd.DataFrame()\n",
    "    indicator_df=pd.DataFrame()\n",
    "    #读取指标数据\n",
    "    indicator_df1=get_period_data(get_m1m2_month(),period='Q')[['m2']]\n",
    "#     indicator_df['m2']=get_m1m2_month()['m2']\n",
    "    \n",
    "#     indicator_df['gdp']=get_gdp_quarter()['gdp']\n",
    "    print (indicator_df1)\n",
    "    \n",
    "    indicator_df['jrh']=indicator_df1['m2']/indicator_df2['gdp']*100\n",
    "    indicator_df=indicator_df.dropna()\n",
    "    \n",
    "    #最后更新日期\n",
    "    lastdate=indicator_df.index[-1].strftime('%Y')\n",
    "    #指标最新数据\n",
    "    bft=indicator_df['bft'].iloc[-1] \n",
    "    #组织图表指标数据\n",
    "    chart_df['%s  %.2f'%(title,float(bft))]=indicator_df['jrh']\n",
    "    #组织指数数据\n",
    "    contrast_df,contrast_title=get_contrast_data(period='Q')\n",
    "    chart_df[contrast_title]=contrast_df    \n",
    "    #去除多余数据\n",
    "    chart_df=chart_df.dropna()\n",
    "    #图表标题,\n",
    "    chart_title='%s （%s）'%(title,lastdate)\n",
    "    chart_df.plot(figsize=(18,7),secondary_y=[contrast_title],title=chart_title,\n",
    "                 linewidth=0.6,mark_right=False,grid=True,style=['b','r'])  \n",
    "    \n",
    "df=get_jrh_chart(title='经济金融化')  \n",
    "df"
   ],
   "cell_type": "code",
   "outputs": [
    {
     "output_type": "error",
     "ename": "NameError",
     "evalue": "name 'get_period_data' is not defined",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-11-6516da0d9217>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     30\u001b[0m                  linewidth=0.6,mark_right=False,grid=True,style=['b','r'])  \n\u001b[0;32m     31\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 32\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mget_jrh_chart\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'经济金融化'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     33\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-11-6516da0d9217>\u001b[0m in \u001b[0;36mget_jrh_chart\u001b[1;34m(code, title)\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mindicator_df\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[1;31m#读取指标数据\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m     \u001b[0mindicator_df1\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mget_period_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mget_m1m2_month\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mperiod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Q'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'm2'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m \u001b[1;31m#     indicator_df['m2']=get_m1m2_month()['m2']\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'get_period_data' is not defined"
     ]
    }
   ],
   "metadata": {},
   "execution_count": 11
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.7.上海银行间同业拆放利率-SHIBOR\n",
    "------------\n",
    "\n",
    "上海银行间同业拆放利率（Shanghai Interbank Offered Rate，简称Shibor），以位于上海的全国银行间同业拆借中心为技术平台计算、发布并命名，是由信用等级较高的银行组成报价团自主报出的人民币同业拆出利率计算确定的算术平均利率，是单利、无担保、批发性利率。\n",
    "\n",
    " - Shibor是新的市场基准利率，Shibor对宏观管理、对于市场参与者了解市场流动性大小，都会提供很重要的指标，Shibor在金融市场的地位，为中国流动性大小提供一个很好的晴雨表和指标，因为最能衡量一个市场流动性的就是基准利率水平；同时，监管当局也可以根据利率变化制定政策，今后很多其他利率尤其是短期利率都会跟Shibor挂钩。\n",
    " - Shibor作为最基本、最市场化的资金价格，对整个宏观基准面、股市、发展金融衍生品、债券价格都具有很重要的意义。金融衍生品的定价与基准利率和风险有关。因此这个利率的变化会直接影响到金融衍生品的价格。比如Shibor利率上升，金融衍生品价格就会下降，关注Shibor的变化，它的变化会影响几乎所有资产的价格，以及其它利率如贷款利率的水平。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def get_shibor_chart(code='SHIBOR',items={'ON':'隔夜拆借率'},title=''):\n",
    "    #图表DataFrame\n",
    "    chart_df=pd.DataFrame()\n",
    "    #读取指标数据\n",
    "    indicator_df=get_shibor_data(code)\n",
    "    #最后更新日期\n",
    "    lastdate=indicator_df.index[-1].strftime('%Y-%m-%d')\n",
    "    #组织图表指标数据\n",
    "    for item,name in items.items():\n",
    "        chart_df['%s  %.2f'%(name,indicator_df[item].iloc[-1])]=indicator_df[item]\n",
    "    #整理数据 \n",
    "    chart_df=chart_df.dropna()\n",
    "    chart_df=chart_df.astype('float32') \n",
    "    #图表标题,\n",
    "    chart_title='%s - %s  %s （%s）'%(code,indicator_dict[code],title,lastdate)\n",
    "    chart_df.plot(figsize=(18,7),title=chart_title,grid=True,\n",
    "        linewidth=0.6,mark_right=False,style=['b','c','r'])  \n",
    "\n",
    "get_shibor_chart(items={'ON':'隔夜拆借率'})\n",
    "get_shibor_chart(items={'1W':'1周','2W':'2周','1M':'1月'})\n",
    "get_shibor_chart(items={'3M':'3月','6M':'6月','1Y':'1年'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.8.十年期国债收益率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#美元指数       \n",
    "def get_c10y_chart(code='C10Y',title=''):\n",
    "    #图表DataFrame\n",
    "    chart_df=pd.DataFrame()\n",
    "    #读取指标数据\n",
    "    indicator_df=get_cy_data(code)[['close']]\n",
    "    #最后更新日期\n",
    "    lastdate=indicator_df.index[-1].strftime('%Y-%m')\n",
    "    #组织图表指标数据\n",
    "    chart_df['%s  %.2f'%(code,indicator_df['close'].iloc[-1])]=indicator_df['close']\n",
    "    #组织指数数据\n",
    "    contrast_df,contrast_title=get_contrast_data(period='D')\n",
    "    chart_df[contrast_title]=contrast_df \n",
    "    #去除多余数据\n",
    "    chart_df=chart_df.dropna()\n",
    "    chart_df=get_period_data(chart_df,period='W')\n",
    "    #图表标题,\n",
    "    chart_title='%s %s - %s （%s）'%(title,code,indicator_dict[code],lastdate)\n",
    "    chart_df.plot(figsize=(18,7),secondary_y=[contrast_title],title=chart_title,\n",
    "                 linewidth=0.6,mark_right=False,grid=True,style=['b','r']) \n",
    "    \n",
    "get_c10y_chart()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.9.波罗的海干散货指数-BDI\n",
    "---\n",
    "\n",
    "BDI是波罗的海干散货指数（Baltic Dry Index）的简称，它是由几条主要航线的即期运费（Spot Rate）加权计算而成，为即期市场的行情的反映，因此，运费价格的高低会影响到指数的涨跌。\n",
    "\n",
    " - 波罗的海综合指数是散装船航运运价指标，而散装船运以运输钢材、纸浆、谷物、煤、矿砂、磷矿石、铝矾土等民生物资及工业原料为主。\n",
    " - 由于散装航运业营运状况与全球经济景气荣枯、原物料行情高低息息相关。因此波罗的海指数被认为是国际间贸易情况的领先指数及经济晴雨表。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'get_period_data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-3834494849c6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     19\u001b[0m                  linewidth=0.6,mark_right=False,grid=True,style=['b','r']) \n\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m \u001b[0mget_bdi_chart\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-10-3834494849c6>\u001b[0m in \u001b[0;36mget_bdi_chart\u001b[1;34m(code, title)\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mchart_df\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;31m#读取指标数据\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[0mindicator_df\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mget_period_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mget_bdi_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mperiod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'W'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'bdi'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m     \u001b[1;31m#最后更新日期\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[0mlastdate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mindicator_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrftime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'%Y-%m'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'get_period_data' is not defined"
     ]
    }
   ],
   "source": [
    "#生成bdI与指数对比图              \n",
    "def get_bdi_chart(code='BDI',title=''):\n",
    "    #图表DataFrame\n",
    "    chart_df=pd.DataFrame()\n",
    "    #读取指标数据\n",
    "    indicator_df=get_period_data(get_bdi_data(code),period='W')[['bdi']]\n",
    "    #最后更新日期\n",
    "    lastdate=indicator_df.index[-1].strftime('%Y-%m')\n",
    "    #组织图表指标数据\n",
    "    chart_df['%s  %.2f'%(code,indicator_df['bdi'].iloc[-1])]=indicator_df['bdi']\n",
    "    #组织指数数据\n",
    "    contrast_df,contrast_title=get_contrast_data(period='W')\n",
    "    chart_df[contrast_title]=contrast_df \n",
    "    #去除多余数据\n",
    "    chart_df=chart_df.dropna()\n",
    "    #图表标题,\n",
    "    chart_title='%s %s - %s （%s）'%(title,code,indicator_dict[code],lastdate)\n",
    "    chart_df.plot(figsize=(18,7),secondary_y=[contrast_title],title=chart_title,\n",
    "                 linewidth=0.6,mark_right=False,grid=True,style=['b','r']) \n",
    "      \n",
    "get_bdi_chart()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.A股证券市场\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1.A股市盈率、市净率、股息率\n",
    "\n",
    " - ROE\n",
    "\n",
    "股本回报率（又称股权收益率、股本收益率或股东回报率或股东回报率，英语：Return On Equity，缩写：ROE），是衡量相对于股东权益的投资回报之指标，反映公司利用资产净值产生纯利的能力。计算方法是将税后净利扣除优先股股息和特殊收益后的净收益除以股东权益。此比例计算出公司普通股股东的投资回报率，是上市公司盈利能力的重要指标。\n",
    "公司通常把盈利再投资以赚取更大回报，ROE正反映了公司这方面的能力，计算办法是把净收益（税后净利扣除优先股股息和特殊溢利）后，除以股东权益。\n",
    "然而，公司的股权收益高不代表盈利能力强。部分行业由于不需要太多资产投入，所以通常都有较高ROE，例如咨询公司。有些行业需要投入大量基础建筑才能产生盈利，例如炼油厂。所以，不能单以ROE判定公司的盈利能力。一般而言，资本密集行业的进入门槛较高，竞争较少，相反高ROE但低资产的行业则较易进入，面对较大竞争。所以ROE应用作比较相同行业。\n",
    "\n",
    " - PE\n",
    "\n",
    "股票的市盈率（Price-to-Earning Ratio，P/E或PER），又称为本益比，指每股市价除以每股盈利（Earnings Per Share，EPS），通常作为股票是便宜抑或昂贵的指标（通货膨胀会使每股收益虚增，从而扭曲市盈率的比较价值）。市盈率把企业的股价与其制造财富的能力联系起来。\n",
    "每股盈利的计算方法，一般是以该企业在过去一年的净利润除以总发行已售出股数。市盈率越低，代表投资者能够以相对较低价格购入股票。\n",
    "投资者计算市盈率，主要用来比较不同股票的价值。理论上，股票的市盈率愈低，表示该股票的投资风险越小，愈值得投资。比较不同行业、不同国家、不同时段的市盈率是不大可靠的。比较同类股票的市盈率较有实用价值。\n",
    "\n",
    " - PB\n",
    "\n",
    "股票的股价净值比（Price-to-Book Ratio，P/B或PBR）又名市账率，指每股市价除以每股净资产，通常作为股票孰贱孰贵的指标之一。\n",
    "股息率（Dividend Yield Ratio）是股息与股票价格之间的比率。在投资实践中，股息率是衡量企业是否具有投资价值的重要标尺之一。\n",
    "盈利收益率其实与股息率颇为类似，仅仅是将股息率分子上的股息换成盈利，便是盈利收益率，因此，其实带出的意思亦相似，盈利收益率主要是比较盈利相对其股价的表现。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.A股盈利收益率\n",
    "-----\n",
    "公式：盈利/股价，盈利收益率其实与股息率颇为类似，仅仅是将股息率分子上的股息换成盈利，便是盈利收益率，因此，其实带出的意思亦相似，盈利收益率主要是比较盈利相对其股价的表现。\n",
    "巴特勒：我总是认为，我们用每股股价除以每股收益的市盈率，而不是用每股收益除以每股股价的盈利收益率作为标准真是太糟了。明白一只股票的盈利收益率是2.5%的实际意义，要比认识到同样这只股票的市盈率是40倍的实际意义要容易多了。\n",
    "格雷厄姆：是的。盈利收益率的概念更加科学，也是一个更合乎逻辑的分析方法。\n",
    "巴特勒：如果分红比率约为50%，那么你可以用盈利收益率的一半来大致推断未来可长期持续的红利收益率。\n",
    "格雷厄姆：是的。基本上，我希望股票投资的盈利收益率能够达到利率的两倍。但是，在大多数年份，利率低于AAA评级债券所支付的5%的利率。因此，我制定了选股标准的两个上限。即使利率低于5%，市盈率最高上限倍数仍为10倍。另一个市盈率最高上限倍数是7倍，即使现在的AAA评级债券超过7%。因此，一般来说，我买入股票的时点是在股票盈利收益率将会达到AAA评级债券利率两倍的时候，最高市盈率倍数在7倍到10倍之间。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'get_PE_plot' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-abc190e38ce6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mget_PE_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'A股市盈率'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mget_EP_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'A股盈利收益率'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mget_EP_GZ_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'A股盈利收益率与10年期国债收益率'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'get_PE_plot' is not defined"
     ]
    }
   ],
   "source": [
    "get_PE_plot('A股市盈率')\n",
    "get_EP_plot('A股盈利收益率')\n",
    "get_EP_GZ_plot('A股盈利收益率与10年期国债收益率')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3.股票新增开户数\n",
    "---------\n",
    "\n",
    "股票开户是投资者在券商处开设的进行股票交易的账户。开立股票账户是投资者进入股市进行操作的先决条件。股票账户分为上海证券公司股票账户和深圳证券公司股票账户两种，投资者可根据需要决定办理一种或两种。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#美元指数       \n",
    "def get_stockcount_chart(code='STOCKCOUNT',title=''):\n",
    "    #图表DataFrame\n",
    "    chart_df=pd.DataFrame()\n",
    "    #读取指标数据\n",
    "    indicator_df=get_stockcount_data()[['new']]\n",
    "    #最后更新日期\n",
    "    lastdate=indicator_df.index[-1].strftime('%Y-%m')\n",
    "    #组织图表指标数据\n",
    "    chart_df['%s  %.2f'%(title,indicator_df['new'].iloc[-1])]=indicator_df['new']\n",
    "    #组织指数数据\n",
    "    contrast_df,contrast_title=get_contrast_data(period='W')\n",
    "    chart_df[contrast_title]=contrast_df \n",
    "    #去除多余数据\n",
    "    chart_df=chart_df.dropna()\n",
    "    #图表标题,\n",
    "    chart_title='%s - %s （%s）'%(title,indicator_dict[code],lastdate)\n",
    "    chart_df.plot(figsize=(18,7),secondary_y=[contrast_title],title=chart_title,\n",
    "                 linewidth=0.6,mark_right=False,grid=True,style=['b','r']) \n",
    "\n",
    "    \n",
    "def get_stockcount_chart2(code='STOCKCOUNT',title=''):\n",
    "    #图表DataFrame\n",
    "    chart_df=pd.DataFrame()\n",
    "    #读取指标数据\n",
    "    indicator_df=get_stockcount_data()[['Final']]\n",
    "    #最后更新日期\n",
    "    lastdate=indicator_df.index[-1].strftime('%Y年')\n",
    "    #组织图表指标数据\n",
    "    chart_df['%s  %.2f'%(title,indicator_df['Final'].iloc[-1])]=indicator_df['Final']\n",
    "    #整理数据 \n",
    "    chart_df=chart_df.dropna()\n",
    "    #图表标题,\n",
    "    chart_title='%s - %s （%s）'%(title,indicator_dict[code],lastdate)\n",
    "    chart_df.plot(figsize=(18,7),title=chart_title,stacked=True,grid=True,\n",
    "        linewidth=0.6,mark_right=False,style=['b','c','r'])   \n",
    "    \n",
    "    \n",
    "get_stockcount_chart(title='新增账户数')\n",
    "get_stockcount_chart2(title='期末账户数')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4.融资融券\n",
    "\n",
    "融资融券又称证券信用交易，是指投资者向具有融资融券业务资格的证券公司提供担保物，借入资金买入证券（融资交易）或借入证券并卖出（融券交易）的行为。\n",
    "包括券商对投资者的融资、融券和金融机构对券商的融资、融券。修订前的证券法禁止融资融券的证券信用交易。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'get_margins_sh' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-6b7fabea6077>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     21\u001b[0m                  linewidth=0.6,mark_right=False,grid=True,style=['b','r']) \n\u001b[0;32m     22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m \u001b[0mget_rz_chart\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-8-6b7fabea6077>\u001b[0m in \u001b[0;36mget_rz_chart\u001b[1;34m(code, title)\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mchart_df\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;31m#读取指标数据\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[0mindicator_df\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mget_margins_sh\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'rzye'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m     \u001b[1;31m#最后更新日期\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[0mlastdate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mindicator_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;31m#.strftime('%Y-%m')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'get_margins_sh' is not defined"
     ]
    }
   ],
   "source": [
    "#美元人民币离岸价      \n",
    "def get_rz_chart(code='MARZ',title=''):\n",
    "    #图表DataFrame\n",
    "    chart_df=pd.DataFrame()\n",
    "    #读取指标数据\n",
    "    indicator_df=get_margins_sh()[['rzye']]\n",
    "    #最后更新日期\n",
    "    lastdate=indicator_df.index[-1]#.strftime('%Y-%m')\n",
    "    #组织图表指标数据\n",
    "    indicator_df['rzye']=indicator_df['rzye']/100000000.0\n",
    "    chart_df['%s  %.2f亿'%(indicator_dict['MARZ'],indicator_df['rzye'].iloc[-1])]=indicator_df['rzye']\n",
    "    #组织指数数据\n",
    "    contrast_df,contrast_title=get_contrast_data(period='D')\n",
    "    chart_df[contrast_title]=contrast_df \n",
    "    #去除多余数据\n",
    "    chart_df=chart_df.dropna()\n",
    "    chart_df=get_period_data(chart_df,period='W')\n",
    "    #图表标题,\n",
    "    chart_title='%s %s - %s （%s）'%(title,code,indicator_dict[code],lastdate)\n",
    "    chart_df.plot(figsize=(18,7),secondary_y=[contrast_title],title=chart_title,\n",
    "                 linewidth=0.6,mark_right=False,grid=True,style=['b','r']) \n",
    "    \n",
    "get_rz_chart()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5.证券交易结算、银证转账\n",
    "-----------\n",
    "\n",
    " - 证券交易结算资金：是指“证券市场交易结算资金监控系统”获取的有经纪业务的证券公司全部经纪业务客户（含部分采取证券公司结算模式的资产管理计划）从事证券交易等的人民币交易结算资金，不包括投资者从事B股交易、融资融券业务等的资金，也不包括证券公司自营、QFII以及采用托管人结算模式的证券公司资产管理计划和公开募集证券投资基金等从事证券交易的资金。\n",
    " - 银证转账：是指在客户交易结算资金第三方存管制度下投资者在银行结算账户和证券资金账户之间的资金划转方式，是引起“证券交易结算资金”变动的重要方式之一。“投资者银证转账引起的资金变动金额”项下的“增加额”是指投资者从银行结算账户转入资金账户的金额；“减少额”是指投资者从资金账户转出到银行结算账户的金额；“净变动额”= “增加额”-“减少额”。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.6.新增信贷\n",
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.7.股指期货主力合约\n",
    "--------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.8.巴菲特指数\n",
    "-----\n",
    "\n",
    " - “巴菲特指标”的计算基于美国股市的市值与衡量国民经济发展状况的国民生产总值(GNP)，巴菲特认为，若两者之间的比率处于70%至80%的区间之内，这时买进股票就会有不错的收益。但如果在这个比例偏高时买进股票，就等于在“玩火”。\n",
    " - 中国巴菲特指数：年末境内上市股票市价总值（A、B股）/国内生产总值（GDP）注：手上暂无中国GNP数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#GDP同比增长与指数对比图        \n",
    "def get_bft_chart(code='',title=''):\n",
    "    #图表DataFrame\n",
    "    chart_df=pd.DataFrame()\n",
    "    indicator_df=pd.DataFrame()\n",
    "    #读取指标数据\n",
    "    indicator_df1=get_market_data()[['total_value']]\n",
    "    indicator_df2=get_gdp_year()[['gdp']]\n",
    "    indicator_df['bft']=indicator_df1['total_value']/indicator_df2['gdp']*100\n",
    "    indicator_df=indicator_df.dropna()\n",
    "    #最后更新日期\n",
    "    lastdate=indicator_df.index[-1].strftime('%Y')\n",
    "    #指标最新数据\n",
    "    bft=indicator_df['bft'].iloc[-1] \n",
    "    #组织图表指标数据\n",
    "    chart_df['巴菲特指标  %.2f'%(float(bft))]=indicator_df['bft']\n",
    "    #组织指数数据\n",
    "    contrast_df,contrast_title=get_contrast_data(period='A')\n",
    "    chart_df[contrast_title]=contrast_df    \n",
    "    #去除多余数据\n",
    "    chart_df=chart_df.dropna()\n",
    "    #图表标题,\n",
    "    chart_title='%s （%s）'%(title,lastdate)\n",
    "    chart_df.plot(figsize=(18,7),secondary_y=[contrast_title],title=chart_title,\n",
    "                 linewidth=0.6,mark_right=False,grid=True,style=['b','r'])  \n",
    "    \n",
    "df=get_bft_chart(title='巴菲特指标')  \n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.9.A股流通市值与M2之比\n",
    "---------\n",
    "\n",
    "中国市场表现通常受限于一个简单的比率——自由流通市值/M2比率(以下简称为“比率”)。当“比率”升逾15%时，市场普遍见顶回落。尽管有时比率能超越15%的水平，但市场最终回天无术(焦点图表1)。因此认为资金流动性决定着中国股市的表现。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#GDP同比增长与指数对比图        \n",
    "def get_15_chart(code='',title=''):\n",
    "    #图表DataFrame\n",
    "    chart_df=pd.DataFrame()\n",
    "    indicator_df=pd.DataFrame()\n",
    "    #读取指标数据\n",
    "    indicator_df1=get_market_data()[['a_circulation_value']]\n",
    "    indicator_df2=get_m1m2_year()[['m2']]\n",
    "    indicator_df['bft']=indicator_df1['a_circulation_value']/indicator_df2['m2']*100\n",
    "    indicator_df=indicator_df.dropna()\n",
    "    #最后更新日期\n",
    "    lastdate=indicator_df.index[-1].strftime('%Y')\n",
    "    #指标最新数据\n",
    "    bft=indicator_df['bft'].iloc[-1] \n",
    "    #组织图表指标数据\n",
    "    chart_df['%s  %.2f'%(title,float(bft))]=indicator_df['bft']\n",
    "    #组织指数数据\n",
    "    contrast_df,contrast_title=get_contrast_data(period='A')\n",
    "    chart_df[contrast_title]=contrast_df    \n",
    "    #去除多余数据\n",
    "    chart_df=chart_df.dropna()\n",
    "    #图表标题,\n",
    "    chart_title='%s （%s）'%(title,lastdate)\n",
    "    chart_df.plot(figsize=(18,7),secondary_y=[contrast_title],title=chart_title,\n",
    "                 linewidth=0.6,mark_right=False,grid=True,style=['b','r'])  \n",
    "    \n",
    "df=get_15_chart(title='自由流通市值与M2比率')  \n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.10.市场人气指标\n",
    "------\n",
    "\n",
    "每月日均换手率，分母为上证A股流通市值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.货币、外汇"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.美元指数\n",
    "\n",
    "美元指数（US Dollar Index，USDX）是通过平均美元与六种国际主要外汇的汇率得出的。美元指数显示的是美元的综合值。一种衡量各种货币强弱的指标。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#美元指数       \n",
    "def get_usdx_chart(code='USDX',title=''):\n",
    "    #图表DataFrame\n",
    "    chart_df=pd.DataFrame()\n",
    "    #读取指标数据\n",
    "    indicator_df=get_usdx_data(code)[['close']]\n",
    "    #最后更新日期\n",
    "    lastdate=indicator_df.index[-1].strftime('%Y-%m')\n",
    "    #组织图表指标数据\n",
    "    chart_df['%s  %.2f'%(code,indicator_df['close'].iloc[-1])]=indicator_df['close']\n",
    "    #组织指数数据\n",
    "    contrast_df,contrast_title=get_contrast_data(period='D')\n",
    "    chart_df[contrast_title]=contrast_df \n",
    "    #去除多余数据\n",
    "    chart_df=get_period_data(chart_df,period='W')\n",
    "    chart_df=chart_df.dropna()\n",
    "    #图表标题,\n",
    "    chart_title='%s %s - %s （%s）'%(title,code,indicator_dict[code],lastdate)\n",
    "    chart_df.plot(figsize=(18,7),secondary_y=[contrast_title],title=chart_title,\n",
    "                 linewidth=0.6,mark_right=False,grid=True,style=['b','r']) \n",
    "    \n",
    "get_usdx_chart()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.美元人民币离岸价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#美元人民币离岸价      \n",
    "def get_uch_chart(code='UCH',title=''):\n",
    "    #图表DataFrame\n",
    "    chart_df=pd.DataFrame()\n",
    "    #读取指标数据\n",
    "    indicator_df=get_uch_data('UCH')[['close']]\n",
    "    #最后更新日期\n",
    "    lastdate=indicator_df.index[-1].strftime('%Y-%m')\n",
    "    #组织图表指标数据\n",
    "    chart_df['%s  %.2f'%(indicator_dict['UCH'],indicator_df['close'].iloc[-1])]=indicator_df['close']\n",
    "    #读取指标数据\n",
    "    indicator_df=get_ucy_data('UCY')[['close']]\n",
    "    #组织图表指标数据\n",
    "    chart_df['%s  %.2f'%(indicator_dict['UCY'],indicator_df['close'].iloc[-1])]=indicator_df['close']\n",
    "    #组织指数数据\n",
    "    contrast_df,contrast_title=get_contrast_data(period='D')\n",
    "    chart_df[contrast_title]=contrast_df \n",
    "    #去除多余数据\n",
    "    chart_df=chart_df.dropna()\n",
    "    chart_df=get_period_data(chart_df,period='W')\n",
    "    #图表标题,\n",
    "    chart_title='%s %s - %s （%s）'%(title,code,indicator_dict[code],lastdate)\n",
    "    chart_df.plot(figsize=(18,7),secondary_y=[contrast_title],title=chart_title,\n",
    "                 linewidth=0.6,mark_right=False,grid=True,style=['b','g','r']) \n",
    "    \n",
    "get_uch_chart()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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